Battery Charakterisation Using Optical Methods

Abstract

The fast advance of electromobility demands the maximizing of energy density and lifetime of any given battery. Such needs for precise information can only be satisfied with the help of adequate simulation models. During the last years, several battery models have been proposed [1, 2, 3]. This publication focuses on physical-chemical battery (p-c-b) models as they allow insights into single electrochemical processes and their effect on the whole system and reliable predictions of the battery behavior. P-c-b models are based on the underlying chemical and physical equations describing the internal battery processes. They require input parameters, which need to be adapted to the specific characteristics of the examined cell and especially the used materials. Input parameters need to be determined in detailed analyses on the opened cell. However, existing methods for model parameterization are insufficient and time intensive: Existing methods are limited in terms of mathematical constraints. In this work, a measurement method for determining the material diffusion parameter is described and applied to different samples based on microscopy. An optical cell is used to track the lithiation progress of graphite anodes introducing color changes upon intercalation. Out of these data, the diffusion coefficient of lithium in graphite is calculated. Furthermore, a parameter study on different graphite anodes is conducted. Single parameters of the graphite anodes are changed to execute a parameter study: the influence of porosity and particle size is shown and analyzed. The results of the parameter study can be used to define the input parameters for the next generation of battery models. Furthermore, the results indicate that the proposed measurement method offers several advantages (as easy adaptable and quick measurable) compared to existing methods. The proposed method can be adapted to other cell types and models. [1] M.Ecker, et all. Parametrization of a Physico-Chemical Model of Alithium-Ion Battery doi:10.1149/2.0551509 J. Electrochem. Soc. 2015 volume 162, issue 9, A1836-A1848 [2] D.Cittanti, et all. Modeling Li-ion batteries for automotive application: A trade-off between accuracy and complexity, doi: 10.23919/EETA.2017.7993213, IEEE Xplore 27.07.2017 [3] A. Kashkooli, Multiscale modeling of lithium-ion battery electrodes based on nano-scale X-ray computed tomography, doi: 10.1016/j.powsour.2015.12.134, J. Power Sources, 2016, vol. 307, p. 496-509